than a predefined maximum value, which is generally slightly larger than the size of the largest non-ground object Zhang et
al., 2003. The only difference between the PM1D and PM2D
algorithms is that the PM2D algorithm uses a two-dimensional square window to perform erosion and dilation. PM1D and
PM2D algorithms use the same parameters. 2.2
MLS Algorithm
Vosselman 2000 developed the MLS algorithm, in which the ground points are detected by comparing the local slope
differences between the adjacent LiDAR points. Irregularly distributed LiDAR points are overlaid with a regularly spaced
grid network. Each grid represents the elevations of LiDAR points. Each
LiDAR point
, , is assigned to the grids with respect to and coordinates. In case where more than one point falls
into a grid, the point with the minimum elevation is chosen. MLS algorithm calculates the slope
0,
between a
0 0
, ,
point and the points in a defined radius.
0, ��
is the maximum slope value between the point
and its neighbours in the radius. If this value is smaller than a
predefined threshold , then the point is labelled as ground.
Otherwise, it is labelled as non-ground and removed. 2.3
ETEW Algorithm
This algorithm uses a gradually increasing search window to separate the ground and non-ground points Zhang and
Whitman, 2005. First, the data is divided into grid cells. In each grid, the points, whose elevations are higher than the
elevation of the point with the minimum elevation, are removed. In the next iteration, the size of the grids is increased and the
minimum elevation value in each grid is recalculated. Then, the points whose elevations are higher than the point with the
minimum elevation with respect to a predefined threshold are removed. Iterations continue by increasing the grid sizes and
threshold values until there is no point to remove in the previous iteration. Let
,
is the elevation of the point
,
in
th
iteration and
th
grid,
,
is the minimum elevation in this grid, and ℎ
,�
is the elevation difference threshold. If
,
−
,
ℎ
,�
, then the point
,
is removed Zhang and Cui, 2007.
2.4 ATIN Algorithm
ATIN algorithm, developed by Axelsson 2000, identifies the ground points with respect to the distance between each point
and generated TIN Triangular Irregular Network surface. First, the data is divided into square grids. Then, the points
which have the minimum elevations in the initial ground data are chosen as seeds. Reference TIN surface is then generated by
using these seeds Zhang and Cui, 2007. Each unclassified ground point candidate point is added to each of the triangles
in TIN. Candidate point is classified with respect to its distance to the triangular surface and to the angle with the vertices of the
triangle. Candidate point is said to be a ground point if the calculated distance and angle are smaller than predefined
thresholds. This process is repeated until all points are labelled as ground and non-ground Axelsson, 2000; Zhang and Cui,
2007; Zhang and Lin, 2013.
3. APPLICATION
3.1 Study Area
A small part of the Karadeniz Technical University KTU campus was chosen as the study area. The campus is in the city
of Trabzon, which is located on the northeast of Turkey. The study area, which has a dimension of 178 m x 410 m and
elevation ranges from 18 m to 83 m, contains flat, sloping and rough regions. There are also non-ground objects such as trees
and buildings. Study area can be seen in Figure 1.
Figure 1. Study area
3.2 Data Preparation
The aerial photos of the study area were taken by using the RICOH GR DIGITAL IV digital camera, which was mounted
on the Gatewing X100 UAV. With a 40-minute flight, 256 aerial photos were captured along 9 flight lines. Before the
flight, 12 ground control points GCP were established in the campus to obtain the georeferenced 3D point cloud. The ground
control points were evenly distributed over the entire campus. The aerial photos were processed and the raw point cloud was
generated by using the Agisoft PhotoScan Professional software. The generated raw point cloud was then densified to
form the medium-density and high-density data. The densities of the raw, medium-density and high-density data were 0.1
pointm
2
, 4.2 pointm
2
and 16.5 pointm
2
, respectively. As a final step, a 25 cm orthophoto was produced for the study area.
3.3 Ground Filtering
PM1D and PM2D algorithms were used to filter the point clouds in PM filtering stage. Main parameters of the PM
algorithm are as follows;
Cell size
� is the size of each grid dividing the point cloud into square arrays. This parameter can be chosen smaller than
the average distance among the points in data Montealegre et al., 2015.
� parameter specifies the elevation
difference threshold. This parameter can be chosen as the average slope of the study area Zhang and Cui, 2007.
Initial Threshold
� is the initial elevation difference threshold used to approximate the error of the points Zhang and Cui, 2007.
Maximum Threshold
is the maximum elevation difference threshold.
Window base
is the base of the exponential function in the window size equation. Table 1 shows the
parameters used in the PM1D and PM2D algorithms.
This contribution has been peer-reviewed. doi:10.5194isprsarchives-XLI-B1-245-2016
246
Filt. Alg.
Data
� �
P M
1 D
Raw 0.4 0.25 0.10 10
2 1 [1,2,4,8,16,32,64,128] M.D 0.3 0.25 0.15
10 2 1 [1,2,4,8,16,32,64,128]
H.D 0.2 0.25 0.15 10
2 1 [1,2,4,8,16,32,64,128] P
M 2
D Raw 0.4 0.21 0.10
10 2 1 [1,2,4,8,16,32,64,128]
M.D 0.3 0.20 0.15 10
2 1 [1,2,4,8,16,32,64,128] H.D 0.2 0.20 0.15
10 2 1 [1,2,4,8,16,32,64,128]
Table 1. The parameters used in the PM1D and PM2D algorithms M.D stands for medium-density and H.D means
high-density The optimum parameters used in the MLS algorithm are given
in Table 2. Experiments revealed that the search radius and maximum slope parameters are the most effective ones. The
parameters used in the MLS algorithm are;
Cell size
� is the size of the grids and generates the minimum
elevation grid.
Minimum distance
refers to the minimum separation between the points allowed in slope computation
Zhang and Cui, 2007.
Maximum slope
is the maximum threshold value for slope. A point is labelled as ground if the
maximum slope with its neighbours is smaller than .
Otherwise, it is labelled as a non-ground point Zhang and Cui, 2007.
Search radius
specifies the neighbours of a point.
Filtering Algorithm
Data
�
MLS Raw 0.4 0.7 0.30 40
M.D 0.4 0.7 0.20 40 H.D 0.5 0.7 0.10 40
Table 2. The parameters used in the MLS algorithm M.D stands for medium-density and H.D means high-density
The optimum parameters used in the ETEW algorithm are shown in Table 3. The slope and iteration number parameters
were found to be the most effective ones with regard to the performance of the ETEW algorithm. The parameters used in
the ETEW algorithm are as follows;
Cell size
� is the initial size of each grid and can be chosen as
the average distance among the points in data Montealegre et al., 2015.
Slope
is the threshold parameter for slope.
Iteration number
indicates how many times the algorithm will iterate Zhang and Cui, 2007.
Filtering Algorithm
Data
�
MLS Raw
0.4 0.4 6 M.D
0.4 0.4 6 H.D
0.3 0.5 8
Table 3. The parameters used in the ETEW algorithm M.D stands for medium-density and H.D means high-density
Table 4 shows the optimum parameters used in the ATIN algorithm. Experiments indicated that the cell size, Z difference
and initial grid size parameters effect the performance of this algorithm most. The parameters used in the ATIN algorithm are
as follows;
Cell size
� is the size of each grid and is used to divide the
point cloud into grids.
Z difference
is the threshold for the elevation difference between each point and triangular surface
Zhang and Cui, 2007.
Initial grid size
�� parameter
generates the initial grid network. This parameter chooses the seed points corresponding to each grid. Axelsson 2000
indicated that this parameter can be set to the size of the largest non-ground object.
Angle threshold
� is the threshold
parameter for the angle between the candidate point and the vertices of the corresponding triangle Axelsson, 2000. Since
the Delaunay triangulation is a time consuming process for large-scaled areas, the ATIN algorithm uses the
� − ℎ
parameter to divide the data into tiles with dimensions and .
Filtering Algorithm
Data
� ��
�
MLS Raw 0.5 0.18 20
5 20
M.D 0.3 0.18 20 5
20 H.D 0.1 0.10 20
5 20
Table 4. The parameters used in the ETEW algorithm M.D stands for medium-density and H.D means high-density
3.4 Accuracy Assessment
